TY - GEN
T1 - Deep Neural Network Based Model Predictive Control for Standoff Tracking by a Quadrotor UAV
AU - Dong, Fei
AU - Li, Xingchen
AU - You, Keyou
AU - Song, Shiji
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The standoff tracking requires an unmanned aerial vehicle (UAV) to loiter in a circular orbit above a target of interest. To achieve it, we propose a deep neural network (DNN) based model predictive control (MPC) for a quadrotor UAV by taking into account the full UAV model and input constraints. Moreover, we propose a new Lyapunov guidance vector (LGV) with tunable convergence rates to plan a reference trajectory for the MPC. The computation latency on the field-programmable gate array (FPGA) at 200MHz is significantly reduced to a constant of 0.12ms. The hardware-in-the-loop (HIL) experiments verify the effectiveness and robustness of our method.
AB - The standoff tracking requires an unmanned aerial vehicle (UAV) to loiter in a circular orbit above a target of interest. To achieve it, we propose a deep neural network (DNN) based model predictive control (MPC) for a quadrotor UAV by taking into account the full UAV model and input constraints. Moreover, we propose a new Lyapunov guidance vector (LGV) with tunable convergence rates to plan a reference trajectory for the MPC. The computation latency on the field-programmable gate array (FPGA) at 200MHz is significantly reduced to a constant of 0.12ms. The hardware-in-the-loop (HIL) experiments verify the effectiveness and robustness of our method.
UR - https://www.scopus.com/pages/publications/85145337715
U2 - 10.1109/CDC51059.2022.9993213
DO - 10.1109/CDC51059.2022.9993213
M3 - 会议稿件
AN - SCOPUS:85145337715
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1810
EP - 1815
BT - 2022 IEEE 61st Conference on Decision and Control, CDC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE Conference on Decision and Control, CDC 2022
Y2 - 6 December 2022 through 9 December 2022
ER -